Consideration about Utilizing Text Architecture for Making Feature Vectors in Classifying Nursing-Care Texts

Consideration about Utilizing Text Architecture for Making Feature Vectors in Classifying Nursing-Care Texts
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利用文本结构构建护理文本分类特征向量的思考

DOI:
10.1109/smc.2013.313
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发表时间:
2013
期刊:
2013 IEEE International Conference on Systems, Man, and Cybernetics
影响因子:
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通讯作者:
R. Sakashita
R. Sakashita
中科院分区:
--
文献类型:
--
作者:
M. Nii;Shouta Miyake;Kazunobu Takahama;A. Uchinuno;R. Sakashita

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由于日本是老龄化最严重的国家之一,提高护理质量对我们来说非常重要。为提高护理质量,提出了基于web的护理质量改进系统,并进行了实验和持续运行。基于网络的系统收集的一种数据是自由式日语文本,称为“护理文本”。护理文本用于评估实际护理过程。为了帮助护理专家对护理文本进行评估,开发了一个计算机辅助护理文本分类系统。本文提出了一种基于短语的特征向量定义方法来对护理文本进行分类。基于依赖关系的特征向量定义在我们之前的工作中已经被提出。作为另一种特征向量定义方法,我们提出了一种基于短语的特征向量定义方法。通过使用依赖关系分析找到短语并将其存储到短语列表中。我们还定义了短语之间的相似性,因为每个短语由某些类型的单词组成。实验结果表明,基于短语的特征向量有助于分类性能的提高。
Since Japan is one of the most aging countries, it is very important for us to improve the nursing-care quality. For improving the nursing-care quality, a Web-based nursing-care quality improvement system have been proposed and operating experimentally and continuously. A kind of collected data by the Web-based system is freestyle Japanese text called "nursing-care texts". The nursing-care texts are used for evaluating actual nursing-care process. In order to assist nursing-care experts in evaluating the nursing-care texts, a computer aided nursing-care text classification system has been developed. In this paper, we propose a phrase based feature vector definition for classifying the nursing-care texts. The dependency relation based feature vector definition has been proposed in our previous work. As another feature vector definition method, we propose a phrase based feature vector definition method. Phrases are found by using the dependency relation analysis and stored into a phrase list. We also define a similarity between phrases because each phrase consists of some kinds of words. From experimental results, we show that our phrase based feature vector contributes the classification performance.
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DOI: --
发表时间: 2007
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